can cloud computing be used for planning? an initial study
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Can Cloud Computing be Used for Planning? An Initial Study. In Proceedings of the 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom-11) , 2011. Authors : Qiang Lu*, You Xu †, Ruoyun Huang†, Yixin Chen† and Guoliang Chen* from - PowerPoint PPT PresentationTRANSCRIPT
Can Cloud Computing be Used for Planning? An Initial StudyAuthors: Qiang Lu*, You Xu†, Ruoyun Huang†, Yixin Chen† and Guoliang Chen* from*University of Science and Technology of China†Washington University in St. Louis
In Proceedings of the 3rd IEEE International Conference on Cloud Computing Technology and Science (CloudCom-11), 2011.
Speaker: Lin Liu from Dept. of ECE, MTU
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Outline
Cloud ComputingMRWPMRWEnhanced PMRWImplementation in Windows AzureExperimental ResultsConclusions
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What is Cloud Computing?
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Cloud Computing
Cloud Computing is a general term used to describe a new class of network based computing that takes place over the Internet
It is a collection/group of integrated and networked hardware, software and Internet infrastructure (called a platform)
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Cloud Computing
Advantages Low cost High availability, scalability, elasticity Free of maintenance
Disadvantages High latency Security
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Parallel Search Algorithms
Search is a key technique for planning The reported parallel algorithms are not
suitable for the cloud environment
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Portfolio Search
A portfolio of algorithms is a collection of different algorithms and/or different copies of the same algorithm running in parallel on different processors or interleaved on one processor
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Monte-Carlo Random Walk (MRW)
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MRW Runtime
Two runs with different random seeds have significantly different running time
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Portfolio Search With MRW
It is common to observe that a MRW run with a different random seed solves the same instance much faster than another one
Such a large variability can benefit a portfolio scheme that makes multiple independent runs and terminates as soon as one run finds a solution
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PMRW
As soon as a processor finds a solution, all other processors will be halted.The solution time of PMRW is the minimum running time of the N independent runs.
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Enhanced PMRW (PMRWms) PMRWms is a strategy that takes in a
candidate configuration set Each processor performs search
independently and simultaneously using the setting
Details are neglected due to time limitation.
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Implementation In Windows Azure
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Experimental Results
Evaluation in a local cloud Evaluation in Windows Azure
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Evaluation In A Local Cloud
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Evaluation In Windows Azure
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Conclusions A portfolio search algorithm which is
suitable for cloud computing is proposed The portfolio of MRW algorithm is
implemented in a local cloud and the Windows Azure platform
The proposed algorithm is economically sensible in clouds and robust under processor failures
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Thanks!
Q & A